Bayesian learning of neural networks adapted to changes of prior probabilities

Authors:
Yoshifusa Ito;Cidambi Srinivasan;Hiroyuki Izumi
Affiliations:
Department of Information and Policy Studies, Aichi-Gakuin University, Aichi-ken, Japan;Department of Statistics, University of Kentucky, Lexington, Kentucky;Department of Information and Policy Studies, Aichi-Gakuin University, Aichi-ken, Japan
Venue:
ICANN'05 Proceedings of the 15th international conference on Artificial neural networks: formal models and their applications - Volume Part II
Year:
2005

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Abstract

We treat Bayesian neural networks adapted to changes in the ratio of prior probabilities of the categries. If an ordinary Bayesian neural network is equipped with m - 1 additional input units, it can learn simultaneously m distinct discriminant functions which correspond to the m different ratios of the prior probabilities.